When deciding whether to use prompt engineering or fine-tuning for an AI model, it can be difficult to determine which method is best. It's generally recommended to start with prompt engineering, as it's faster and less resource-intensive. To help you choose the right approach, here are the key benefits of prompting and fine-tuning:
- A generic model can work out of the box (the task can be described in a zero shot fashion)
- Does not require any fine-tuning data or training to work
- Can easily be updated for new workflows and prototyping
Check out our prompting guide to explore various capabilities of Mistral models.
- Works significantly better than prompting
- Typically works better than a larger model (faster and cheaper because it doesn't require a very long prompt)
- Provides a better alignment with the task of interest because it has been specifically trained on these tasks
- Can be used to teach new facts and information to the model (such as advanced tools or complicated workflows)
Fine-tuning has a wide range of use cases, some of which include:
- Customizing the model to generate responses in a specific format and tone
- Specializing the model for a specific topic or domain to improve its performance on domain-specific tasks
- Improving the model through distillation from a stronger and more powerful model by training it to mimic the behavior of the larger model
- Enhancing the model’s performance by mimicking the behavior of a model with a complex prompt, but without the need for the actual prompt, thereby saving tokens, and reducing associated costs
- Reducing cost and latency by using a small yet efficient fine-tuned model